Each topic below explains the evidence, the business problem it creates, how GLOBEIR solves it, the benefits to your organisation, and how your data stays private and secure.
1. Disease Surveillance and Outbreak Hotspot Mapping
India's Integrated Disease Surveillance Programme moved onto the Integrated Health Information Platform (IHIP) in stages. The IDSP segment was soft-launched in seven states in November 2018 to give policy makers near-real-time data for detecting outbreaks, with 32,000 people trained at block level, 13,000 at district level and 900 at state level [1]. WHO reported the pan-India roll-out in April 2021: the platform covers more than 30 diseases, integrates data from public and private hospitals, laboratories and research centres, and is designed to exchange animal health, environmental health and climate data in support of a One Health approach [2].
The volume of signals is now large. NCDC reports that 1,84,895 reporting units were covered under syndromic (S form) surveillance in 2025, with separate presumptive (P form) and laboratory-confirmed (L form) streams, and that an average of 40 outbreaks a week are reported to the Central Surveillance Unit. Reported outbreaks rose from 554 in 2020 to 1,862 in 2023 and 3,020 in 2024, with 2,285 in 2025 [3]. Whenever illness rises in an area, Rapid Response Teams investigate, and analysis and response sit with the state and district surveillance units [3].
Spatial analysis fits naturally on top of this system. Once aggregated counts are tied to a sub-centre area, village, ward or block, epidemiologists can compare each area with its own history and its neighbours, detect clusters that cross administrative lines, and overlay rainfall, water supply, markets or migration routes that may explain a rise.
Business problem
District and state surveillance units receive a steady stream of weekly and daily reports, but they often review them as tables by facility or block. A gradual rise spread across several neighbouring villages, or a cluster that sits on a district boundary, does not stand out in a table. By the time an outbreak is obvious, Rapid Response Teams are reacting rather than getting ahead of it, and officers have little time to look for patterns across hundreds of reporting units.
Our solution
- A health base map that links every reporting unit to sub-centre areas, villages, wards, blocks and districts, built with GIS mapping and checked in the field where locations are uncertain.
- Hotspot and space-time cluster detection on aggregated counts by syndrome and week, using geospatial data science.
- A WebGIS alert dashboard and heatmap view for surveillance units, with drill-down from state to block and overlays such as rainfall and water sources.
- Optional outbreak investigation forms in the My GLOBEIR app for Rapid Response Teams, capturing area-level findings with location and time.
Benefits
- Earlier sight of unusual rises, including clusters that cross block or district boundaries.
- A shared map for epidemiologists, programme officers and Rapid Response Teams, so alerts lead to action in specific places.
- A consistent way to triage a growing number of alerts; in India, reported outbreaks rose more than five-fold between 2020 and 2024 [3].
- A base that can carry animal health, environmental and climate layers, in line with the One Health direction of IHIP [2].
Privacy & data security
- Analysis uses counts aggregated to an agreed area unit. Patient names, contact details, addresses and record numbers are never placed on the map.
- Small counts can be suppressed or grouped so that no individual or household can be singled out, and areas are shown without stigmatising labels.
- Any line-list processing stays inside the department's own environment, with role-based access and audit logs.
2. Health Facility Access and Gap Analysis
The Indian Public Health Standards 2022 set clear population norms. A rural PHC is to serve 30,000 people in the plains and 20,000 in hilly and tribal areas, an urban PHC 50,000, and a multispecialty polyclinic 2.5 to 3 lakh people [4]. A rural CHC is to serve 1,20,000 people in the plains and 80,000 in hilly and tribal areas "and/or" follow a time-to-care approach, and should be set up at block, taluka or tehsil level [5]. IPHS 2022 also carries forward the National Health Policy 2017 principle that time to care should be no more than 30 minutes [4].
Population norms say how many facilities an area should have; travel-time mapping says whether people can reach them. A global study in Nature Medicine combined facility locations from OpenStreetMap, Google Maps and academic datasets to map travel time to health facilities. It found that 8.9 per cent of the world's population (646 million people) cannot reach care within one hour even with motorised transport, and 43.3 per cent (3.16 billion people) cannot do so on foot, with far longer travel times in rural areas [6].
In practice, a facility gap analysis maps every facility and settlement, models travel over roads, footpaths, terrain and river crossings, and then counts how many people each facility serves within 30 or 60 minutes. Settlements outside every catchment, and facilities whose catchments overlap heavily, become visible at once.
Business problem
State and district health planners must decide where to add, upgrade or strengthen facilities each year within fixed budgets. Decisions are often based on facility counts against population, so facilities can cluster along main roads while hill, forest and riverine settlements stay far from care. Without travel-time evidence, it is hard to justify one site over another or to show that a new facility will actually close a gap.
Our solution
- Verified facility and settlement maps, with field checks through the My GLOBEIR app where registry locations are missing or wrong.
- Walking and motorised travel-time catchments for each facility level, using network analysis over roads and terrain.
- Comparison of catchment populations with IPHS norms and the 30-minute time-to-care principle [4][5], with gaps and overlaps shown on a WebGIS map.
- Ranking of candidate sites for new or upgraded facilities and outreach sessions by the population they bring within reach.
Benefits
- Facility and outreach plans backed by measured access, not only headcount.
- Clear evidence for budget proposals, showing how many people each option brings within the time-to-care principle [4].
- A repeatable method that can be rerun as roads, settlements and facilities change.
- Industry example: global travel-time maps show that walking access is far weaker than motorised access, especially in rural areas [6], which helps target transport support and outreach.
Privacy & data security
- Facility access analysis uses public facility locations and area-level population; no patient data is needed.
- Where facility utilisation data is added, it is summarised by facility or area before analysis.
- Facility maps for government clients can be hosted on MeitY-empanelled government cloud or the department's own servers.
3. Vector-Borne Disease Risk with Remote Sensing
India has made large gains against malaria. Cases fell from 11,69,261 in 2015 to 2,27,564 in 2023 and deaths from 384 to 83, and in 2023, 122 districts reported zero malaria cases [8]. The National Framework for Malaria Elimination aims for zero indigenous cases by 2027 and elimination by 2030, and the National Strategic Plan for 2023-2027 introduced enhanced surveillance and real-time data tracking through IHIP [8]. As transmission shrinks, the remaining cases concentrate in specific pockets, which makes precise targeting more important.
Satellite data can show where conditions favour mosquito breeding. A 2025 study in GeoHealth used Landsat-8 imagery from 2018 to 2021 for Cuttack district in Odisha, calculating water, moisture, vegetation and land surface temperature indices to demarcate waterlogged areas and breeding sites. It classified 11,730 hectares as high risk, 28,054 hectares as medium risk and 12,670 hectares as low risk for mosquito-borne diseases, for use in planning control and prevention [9].
Risk maps from imagery do not replace entomological surveys or case data; they help decide where to look first. Combined with rainfall, elevation, land use and aggregated case counts, they give vector control teams a seasonal map of where to concentrate effort.
Business problem
Indoor residual spraying, net distribution, larval source management and active case detection are expensive and labour-intensive. When cases are low and scattered, spreading these measures evenly across a district wastes resources, while a missed pocket of transmission can undo years of progress. Programme officers need to know which villages and wards carry the highest risk this season.
Our solution
- Seasonal remote sensing of water, moisture, vegetation and land surface temperature indices, following methods such as those used in Cuttack [9].
- Integration with rainfall, elevation, land use, drainage and aggregated case data into village or ward risk zones using AI and machine learning where enough history exists.
- Field verification of potential breeding sites with geo-tagged photos in the My GLOBEIR app.
- Risk maps and progress views for district vector-borne disease officers on a WebGIS dashboard.
Benefits
- Vector control and case detection directed to the highest-risk zones first.
- Seasonal updates that follow monsoon and post-monsoon changes in breeding conditions.
- Support for elimination goals of zero indigenous malaria cases by 2027 [8].
- Industry example: satellite indices identified distinct high, medium and low-risk zones across a whole district in Odisha [9].
Privacy & data security
- Satellite imagery and environmental indices contain no personal data.
- Case data is used only as aggregated counts per village or ward; household-level case locations are not shown on shared maps.
- Field photos of breeding sites are taken of places, not people, and are stored with role-based access.
4. Immunisation and Campaign Microplanning
Every immunisation campaign rests on a microplan: a list of settlements, their target populations, session sites, teams and days. When the microplan misses a settlement, the children there are missed. Nigeria's polio programme found this the hard way. When officials sampled 25 settlements in one ward and compared them with satellite imagery, numerous settlements were wrongly located or named and some were missing altogether. The digital maps that followed, covering 10 northern states over two years, found settlements of up to 1,000 people that had not been visited by vaccination teams in many years. Polio cases fell from 99 in 2012 to zero, and Nigeria was certified polio-free in June 2020 [10].
The approach has scaled. For a 2021 non-polio supplementary immunisation campaign, GRID3 produced 9,308 ward-level maps using settlement points, gridded population, administrative boundaries and infrastructure such as health facilities, schools and markets. They were distributed by Nigeria's primary health care agency through WHO state offices, and settlements within 1 km were clustered to place fixed and temporary vaccination posts [11]. A Gavi evidence review of GIS mapping for immunisation found 31 relevant studies, most with promising results. In one measles campaign, GIS-based microplans showed 8.2 per cent variation in target population against 19.6 per cent for traditional walk-through plans, and 10 of 11 enumeration areas with zero coverage were in states that did not use GIS maps [12].
Business problem
Microplans are often prepared on paper or from old sketches, and updated in a rush before each round. New hamlets, construction and brick-kiln sites, seasonal migrant camps and fast-growing peri-urban areas are easy to leave out. Supervisors cannot easily see which areas each team covered, and mop-up rounds are planned from tallies rather than maps.
Our solution
- Settlement and population maps built from satellite imagery and official data with remote sensing and GIS mapping.
- Field verification of settlements, session sites and population counts in the My GLOBEIR app, with offline capture for areas without network coverage.
- Digital microplans with team areas, routes and target populations, using digital micro mapping and mobile GIS.
- Campaign dashboards showing aggregated coverage by area to plan mop-up, delivered on WebMap.
Benefits
- Microplans that account for every mapped settlement, including new and temporary ones.
- More accurate target populations for vaccine, logistics and team planning; industry example: 8.2 per cent versus 19.6 per cent variation in Nigeria [12].
- Better visibility of missed areas for mop-up; industry example: unmapped settlements of up to 1,000 people found in northern Nigeria [10].
- A reusable base map for routine immunisation, vitamin A, deworming and other outreach.
Privacy & data security
- Microplans work at settlement and area level; children's names and records stay in the programme's own registers.
- Field team location tracking in the My GLOBEIR app is optional and opt-in with a persistent notification, and is used for work purposes only.
- Coverage is reported as aggregated figures by area, with no labelling of communities.
5. Ambulance and Emergency Response
The Operational Guidelines on National Ambulance Services 2026, released by the Union Health Minister in June 2026, give India its first comprehensive national framework for planning, operating and monitoring ambulance services across all states and union territories [7]. They cover ambulance categorisation, population-based fleet deployment, staffing, equipment and performance monitoring, and require all ambulances to conform to AIS-125 standards [7].
The guidelines are explicitly spatial. They call for Integrated Command and Dispatch Centres supported by GPS-enabled ambulance tracking, intelligent dispatch and real-time monitoring dashboards, and for progressive integration with the 112 emergency number. Through GIS-based mapping of healthcare facilities, referral centres, ambulance stations, accident-prone areas, bed availability and critical care preparedness, dispatchers can identify the nearest and most appropriate facility. They also recommend evidence-based deployment of ambulances by analysing call trends, referral patterns, traffic density, accident hotspots, geographical constraints and population distribution [7].
Business problem
Ambulance services must meet response-time expectations across dense cities, highways and remote blocks with a limited fleet. Base locations set years ago may no longer match where calls come from, and dispatchers without a live map can send a vehicle that is not the nearest, or take a patient to a facility that cannot treat them. Programme managers need evidence to defend fleet and base decisions.
Our solution
- Mapping of past call locations, bases, hospitals by capability, roads and accident locations with GIS mapping.
- Response-time models that test alternative base locations and fleet sizes, using geospatial data science.
- Live tracking of vehicle position and status integrated into a dispatch map showing the nearest suitable facility.
- Performance dashboards by block and ward through administration monitoring.
Benefits
- Base and fleet decisions grounded in call demand, travel time and accident locations, as the NAS 2026 guidelines recommend [7].
- Dispatchers see the nearest available ambulance and the most appropriate facility on one screen [7].
- Response-time reporting by area that shows where service falls short.
- A map-based setup that can link to the 112 integration envisaged in the guidelines [7].
Privacy & data security
- Call locations are used in aggregated form (grid cells or wards) for planning; caller identity is not needed.
- Live dispatch data is restricted to command centre roles, with audit logs of access.
- Vehicle and crew tracking is limited to duty hours and work purposes.
6. Environmental Health: Heat and Air Quality
Air pollution is one of India's largest health risks. A Global Burden of Disease study in The Lancet Planetary Health estimated 1.67 million deaths in India in 2019 attributable to air pollution, 17.8 per cent of all deaths, including 0.98 million from ambient particulate matter and 0.61 million from household air pollution. The death rate from ambient particulate matter pollution rose by 115.3 per cent between 1990 and 2019, and lost output cost an estimated US$36.8 billion, 1.36 per cent of GDP [13].
Heat is a growing seasonal emergency. NCDC's 2026 advisory asks states to submit daily data on heatstroke cases and deaths, emergency attendance and total deaths on the IHIP portal under the National Programme on Climate Change and Human Health from 1 March 2026, to investigate clusters of heat-related deaths, to share IMD heatwave warnings with health facilities and vulnerable populations, and to consider public cooling and drinking water facilities. Health sector heat action plans are to be updated and shared with State Disaster Management Authorities [14].
Satellite-derived land surface temperature, green cover and built-up density show which wards heat up most. Combined with population, facility locations and aggregated heat illness or respiratory case counts, these layers produce local risk maps for preparedness.
Business problem
Heat action plans and air quality responses are usually set at city or district level, while exposure varies sharply from one ward or village to the next. Health departments have to decide where to place cooling points, drinking water, ORS stocks and outreach, and which hospitals to prepare first, often with only one weather station and one air quality monitor for a large area.
Our solution
- Land surface temperature, green cover and built-up density layers from environmental remote sensing.
- Integration of air quality monitor readings, IMD warnings, population, facility locations and aggregated heat illness counts into ward or village risk indices.
- Risk maps and seasonal dashboards on WebGIS to support heat action plans and advisories [14].
- Field validation of cooling points and drinking water facilities through the My GLOBEIR app.
Benefits
- Heat and air quality measures directed to the neighbourhoods most exposed.
- Evidence for health sector heat action plans and their links to State Disaster Management Authorities [14].
- Hospital preparedness planned around where heat illness is most likely.
- A clear, area-level view of air pollution, which was linked to an estimated 17.8 per cent of deaths in India in 2019 [13].
Privacy & data security
- Heat and air quality layers are environmental and contain no personal data.
- Heat illness surveillance now uses patient-level line lists [14]; GLOBEIR works only with counts aggregated to ward or village unless the department processes line lists in its own environment.
- Risk maps describe places and exposure, not individuals or communities.